• Define Data Platform Strategy: In partnership with senior leadership, define the vision, roadmap, and operating model for AssetWatch’s data platform, ensuring it supports analytics, product development, and AI initiatives across the organization. • Lead the Data Platforms Organization: Build, lead, and develop the data platforms team, establishing clear goals, accountability, and performance expectations. Develop managers and engineers while fostering a culture of ownership, collaboration, and continuous improvement. • Set Organizational OKRs: Partner with executive leadership to define strategic OKRs for the data platform organization and translate them into measurable goals for teams and individuals. • Own Data as a Company Asset: Establish the governance framework that ensures data across the organization is well-defined, trusted, and properly managed, including dataset ownership, data definitions, lineage, access controls, and lifecycle management. • Data Platform Architecture: Design and evolve AssetWatch’s cloud data platform using AWS technologies such as S3, Glue, Athena, Redshift, and Lambda to support scalable analytics, reporting, and AI workloads through MCPs. • Data Ingestion & Pipelines: Oversee the development and reliability of data pipelines ingesting information from product telemetry, APIs, and enterprise SaaS systems. • Curated Data & Data Modeling: Lead the creation of trusted datasets used for reporting, operational decision making, and machine learning. • AI-Ready Data Infrastructure: Ensure the company’s data platform is structured to support AI and machine learning initiatives by enabling reliable, well-modeled data inputs for ML pipelines and advanced analytics. • Enterprise Systems Data Integration: Partner closely with Enterprise Applications leadership to ensure systems such as Salesforce, finance platforms, and customer success tools integrate cleanly into the data platform. • Data Quality & Reliability: Establish monitoring and operational processes that ensure data accuracy, pipeline reliability, and data freshness. • Security, Privacy & Compliance: Work with IT and Security teams to ensure the data platform meets SOC2 and privacy requirements including access control, encryption, audit logging, and retention policies. • Operational Efficiency: Eliminate manual reporting workflows and redundant integrations by creating scalable, automated data pipelines and shared data models. • Cross-Functional Leadership: Lead highly visible initiatives that align teams across engineering, product, operations, and leadership to improve data reliability and organizational decision-making.

• Architect enterprise-scale GenAI data platforms for ingestion, transformation, enrichment, and serving of structured and unstructured data • Design scalable pipelines for enterprise knowledge ingestion from diverse data sources including documents, SaaS platforms, knowledge bases, collaboration tools, and databases • Define architecture for metadata extraction, chunking, enrichment, embeddings generation, and knowledge preparation workflows • Design AI-ready data models and storage strategies for vector, graph, and hybrid knowledge systems • Architect scalable unstructured data processing pipelines for text, images, PDFs, tables, and multimodal content

Role Description We are seeking a detail-oriented and dependable Remote Data Entry Specialist to join our growing team. In this role, you will be responsible for accurately entering, updating, and maintaining information within company databases and systems. This is a fully remote position offering flexibility, paid training, and opportunities for professional growth. Salary: $25–$30 per hour Weekly pay available depending on employer policies. Responsibilities - Enter and update data accurately into company databases and spreadsheets - Review records for errors, inconsistencies, and missing information - Organize and maintain digital files and documentation - Verify data accuracy by cross-checking source materials - Communicate with internal teams to resolve discrepancies - Follow confidentiality and company data protection procedures - Meet daily and weekly productivity goals Qualifications - High school diploma or equivalent - Strong attention to detail and organizational skills - Basic computer knowledge and typing proficiency - Ability to work independently in a remote environment - Strong communication and time-management skills - Reliable internet connection and computer/laptop access Preferred Qualifications - Previous data entry, administrative, or customer support experience preferred but not required - Familiarity with Microsoft Excel, Google Sheets, and online databases is a plus Benefits - Fully remote position - Flexible scheduling options - Paid training provided - Career advancement opportunities - Supportive and collaborative work environment - Work-life balance with remote flexibility

• Design, build, and operate production data pipelines that power Leadfeeder's product features — from ingestion through enrichment, processing, and serving. • Build and maintain streaming and real-time ingestion systems that move event data through the platform at scale and with low latency. • Own the cloud infrastructure underpinning the pipelines — compute, storage, networking, security, observability — designed and managed as code. • Collaborate with product and ML engineers to deliver datasets and pipelines that power product-facing features and AI/ML workflows. • Implement data quality, observability, and reliability controls across the pipelines so issues are caught early, incidents are short, and downstream teams can trust the data. • Drive engineering practices across the team: code review, testing, CI/CD for data, infrastructure-as-code, performance tuning, and cost discipline. • Partner with engineering, product, and ML teams to translate product requirements into scalable, well-documented data systems.